Time series are data observed over time (either in continuous time or at discrete time periods).

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Why are “time series” called such?

Why are “time series” called such? Series means sum of a sequence. Why is it time Series, not time sequence? Is time the independent variable?
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Filtering time periods where relationship is swiched on/off

Sorry for the "unmathematical" formulation of the problem to come, but I am not sure where to place my problem: Suppose there exists a relationship between the variables x and y. I can observe both ...
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18 views

Bayesian Time Series Analysis Source

Is anyone able to recommend a source that covers Bayesian time series analysis in Winbugs?
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26 views

How do panel regression estimates differ from those obtained from multiple time series regressions?

I am trying to familiarise myself with panel regression techniques and I would like to know how the parameter estimates obtained from a panel regression model differ from those obtained from multiple ...
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1answer
45 views

Forecasting in Stata

I am working with time series data and fitting an autoregressive model using OLS. For reference, here is my price data for the commodity (I am not sure how to better format data for this site): ...
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26 views

Dependent variable is non-stationary and independent variable is stationary - residual series?

I ran a regression model where dependent variable is non-stationary (I know this is wrong) and my independent variable is stationary...I find that the residual series are stationary... how is it ...
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23 views

How to make series stationary when dependent variable is log(y)

I need some help in understanding the following: I have a time series data (y) that I am using to run regression models. However, my dependent variable is log(y). Should I test for stationarity of ...
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1answer
32 views

Is this a job for mixture of experts regression or semi-hidden markov models or something else?

Data I have several thousand timeseries each comprising around 365 data points. Browsing through a few of them, it looks like each timeseries consists of several regimes (different number f regimes ...
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Base sales in multivariate time series | MCMC model

I have been looking around online for good resources that explain how one would go about calculating base sales when preforming marketing mix modeling. I was told by a colleague that essentially they ...
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26 views

Model selection and performance evaluation using cross-validation for time series with missing values

So my task is to select and evaluate a statistical model (random forest, boosted trees, neural networks etc.) for a time series with missing values around 10 years long. One of the goals of that is to ...
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37 views

why the non-seasonal and seasonal parts are multiplied in ARIMA models?

I would like to understand why the non-seasonal and seasonal parts are multiplied in Seasonal ARIMA models. To be more specific: when we use the Seasonal ARIMA model we assume a multiplicative ...
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54 views

Comparing data that has been recorded on two devices

I have two data loggers which are recording a physiological signal. Device A is a system that has been in place for many years, and records data ~once/minute. Device B is a prototype device which ...
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1answer
95 views

Daily Ticket Sales

I looked around to see if there was a similar question, but couldn´t find one. I apologize if there is one and I missed it. I have the amount of ticket sales per day for 10 different events. The ...
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Can you specify AR(p) structure for cyclic spline in mboost?

Suppose I fit want to fit a boosted GAM using mboost:gamboost to time series data. Is it possible to specify an AR(p) structure for the cyclic component following a ...
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Best method for short time-series

I have a question related to modeling short time-series. It is not a question if to model them, but how. What method would you recommend for modeling (very) short time-series (say of length $T \leq ...
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15 views

Correct df in longitudinal linear mixed model?

I am having trouble understanding how to correctly apply a linear mixed model to my data to measure the effect of wifi exposure. 4 beehives contained sensors collecting data on temperature (DHT22_t, ...
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1answer
76 views

Nonlinear Autoregressive model parameter estimation from time series

I'm working on a nonlinear multivariate autoregressive model of order 1 (markovian). It is a discrete-time dynamical system which models exchange of mass between compartments in a compartmental model ...
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10 views

An observation too short / missing data in panel

I have a panel data set with 7 lines or concepts from 1948 to 2013. However there is an 8th concept that I need that is only from 1993-2010. Is there a way in which I could estimate this variable's ...
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1answer
35 views

How should I represent validity of a population prediction?

I am looking to report on the validity of a predictive non-linear population model for which I only have the output prediction p(t) and the time for which the ...
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1answer
42 views

Time series with multiple subjects and multiple variables in R

I'm having trouble finding a time series technique to deal with a data set I am working on. It contains multiple subjects and multiple variables, not all of which will likely be part of the time ...
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33 views

Forecast Error Variance Decomposition with restricted VAR model

For conducting Forecast Error Variance Decomposition (FEVD) on a restricted VAR model I use the fevd method in the package vars ...
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22 views

Obtaining adjusted proportions with logistic regression

Can I obtain adjusted proportions of a binary variable by using logistic regression? I have a binary variable (normal/abnormal), which I'd like to obtain adjusted prevalence for (i.e the proportion ...
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2answers
55 views

fitting a cubic polynomial to a trend component of time series

I have 295 observations of two variables, of which here are a few: ...
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2answers
72 views

How can I model a binary outcomes in time series using logistic regression?

My data has a binary outcome (attack or not attack), day (20 day in repeated measured design) and some covariates (nestling’s movement). The objectives of my experiment are testing the effect of time ...
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33 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
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Do you see trends in my residual plots?

Do you see trends in my residual plots? These residuals plot show the standardized residuals against fitted values, origin period, calendar period, and development period. The patterns in any ...
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Residual based bootstrap autoregressive series in MATLAB

I have defined the model as follows. Let $$y_1 = 0$$ and $$ y_i = \alpha + \beta y_{i-1} + \epsilon_i $$ for $i_2\ldots i_T$, where $\alpha$ and $\beta$ are the estimated coefficients and ...
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60 views

Help understanding how the cointegration equation for VECM models are derived

I am learning about Vector Error Correction Models from Sean Becketti's "Introduction to Time Series using Stata". While I know how to run the Stata commands to estimate the VECM, I have no idea why ...
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Using seasonality in a model

Suppose you are modeling sales of a prepackaged good and suppose there are seasonal periods in the time series. Also suppose that the brand of the prepackaged good you are modeling comprises the ...
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63 views

Prediction based on multiple time series - Python

I have 3 predictors and 1 variable that represents ground truth. They all are linked time series. My purpose is with the 3 predictors to try to forecast the ground truth data. For example : ...
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How to compare and cluster sets of daily time series?

I have multiple dataframes each representing traffic speed for each day of the year (366 dataframes for 366 days of the year). The raws of the dataframe are timestamp from 00:00 to 23:55 at 5 minute ...
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197 views

Analyse ACF and PACF plots

I want to see if I am on the right track analysing my ACF and PACF plots: Background: (Reff: Philip Hans Franses, 1998) As both ACF and PACF show significant values, I assume that an ARMA-model ...
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1answer
48 views

Bootstrapping at group level (time series data)

I have time series of continuous measurements of two different variables $x(t)$ and $y(t)$ measured at times $t_i$. I measured those variables for different subjects (with different characteristics) ...
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79 views

What's the model representation for the first difference of a local level model?

This is my first exercise for space state models and I've a few questions I'd need to resolve before I actually start doing the exercise. Unfortunately, I'm self teaching (I have no professor to ask) ...
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How to evaluate a Bayesian forecast?

Suppose that I have a predictive posterior, which is an attempt to predict some one-step ahead forecasted value $\hat{y}_{T+1}$. How do I assess if my posterior has done a good job or not? If we had ...
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Comparison of Non-Stationary Time Series Trends

I am trying to compare two readings of the same occurrences from two different sources, forming two time series. I would like to assign a metric to their similarity/dissimilarity, but the method I am ...
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Suitable model for predicting mean output over time

I have 3 years of yearly historic data on the results of a harvest (of truffles) from multiple different areas involving multiple individuals harvesting in each area. The dataset contains: name of ...
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Is it possible to simplify linear regression if one of the variables does not have errors and is equispaced?

Specifically, I have a time series with fixed $\Delta t$ of the following form: $x_0, x_1, x_2, ... x_n$ and $t_0, t_1, t_2, ... t_n$ where $t_{i+1}-t_i =\Delta t $. I'm interested in the improved ...
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39 views

Maximum value of d in ARIMA model

I am trying to model a data series using ARIMA model. The series seems non stationary because the acf decays very gradually.Even after differencing two times, the values of p and q are coming as high ...
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Regressing nonstationary on stationary variable

I am trying to empirically estimate the coefficient for the Okun's law as a relationship between output growth and unemployment. I am using the simple gap version, where I regress real output growth ...
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1answer
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Get groups in time series with categorical data in R for use in gts

I have sales data organised in a table with 6 columns (4 for the location and type data, and 2 for the dates and the quantity sold), and 24 rows for each category representing the sales over 24 months ...
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Forecasting in r using ets() of forecast package..seasonality and trend not detected

I have tried forecasting in R using ets(). I let ets choose the best model for my data. The problem is i observed that eventhough the data shows an increasing trend and exhibits seasonality, ets is ...
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AR(q) model with F-test

I am wondering that if we have an AR($q$) model for time series: $$X_i=\beta_1X_{i-1}+..+\beta_{p}X_{i-p} + \beta_{p+1} X_{i-p-1} +...+\beta_{q} X_{i-q}+\epsilon_i,\epsilon_i \;\text{iid}\; ...
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72 views

Statistically Approximating Clicks From Limited Data

Assume a business started in January 2014. I have the following daily data (from June 2014 to December 2014): 1. Number of people who joined the website; 2. Number of people who left the website; ...
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27 views

How to remove level shifts and pulses from time series?

I am interested in describing seasonal patterns in several time series and then seeing if they are related. My approach is to fit regression models with an indicator variable for each season which ...
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18 views

MANOVA on autocorrelated variables

I am trying to find out if signals 1 and 2, can explain signals 3 to 10. All signals are continuous and time-varying and are rather strongly autocorrelated. Signals 3 to 10 (my dependent variables) ...
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1answer
39 views

Using monthly product usage data to predict customer churn

I've been reading tons of papers detailing methods on predicting customer attrition, but none of them have mentioned using product usage data over time. We keep detailed logs of how many times User A ...
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1answer
42 views

What is the auto-covarriance of a stationary AR1 process?

Say a stationary AR(1) process is given by: $$ X_t = c + \phi X_{t-1} + \epsilon_t $$ where $ \epsilon_t $ is a white noise process with zero mean and constant variance $ \sigma^2 $. Wikipedia ...
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Finding occurrences of specific patterns in time series

I have to locate occurrences of Cyllinder, Bell and Funnel patterns in univariate time series $X$ of gamma-ray sensoring. This is a specific case of the general CBF synthetic problem found in a few ...